How to Train and Deploy a ResNet-50 Model

ResNet-50 is an established computer vision model architecture used for image classification, which can be trained and deployed using Roboflow, a platform that facilitates the transformation of raw data into trainable datasets. This guide details the process of training a ResNet-50 model to classify defects in juice boxes, such as loose or incorrectly oriented straws, by preparing and annotating a dataset within Roboflow, generating a dataset version, and utilizing the Roboflow Workflows and Inference tools for deployment. Users can either fork a pre-existing dataset from Roboflow Universe or upload their own data, annotate it, and create class labels before training the model. Once the model is trained, it can be deployed using Roboflow’s Inference server, allowing users to run custom workflows and test the model’s performance, as demonstrated with a successful classification of a juice box defect. The guide encourages experimentation with different workflow configurations to enhance the deployment process.